Can Gemma 3 12B run on Tesla P40 24GB?

YES — Runs Great

A82Great

Estimated from fit model

Gemma 3 12B needs ~15.8 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~29 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 15.8 GB, 29.3 tok/s, Runs well

15.8 GB required24.0 GB available

66% VRAM used

Fit status

Runs well

Decode

29.3 tok/s

TTFT

6611 ms

Safe context

43K

Memory

15.8 GB / 24.0 GB

Memory breakdown

Weights7.3 GB

KV Cache4.9 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsGemma 3 12B on Tesla P40 24GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers))  # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
    """Sort a list using the quicksort algorithm.

    Args:
        arr: List of integers to sort.

    Returns:
        A new sorted list.
    """
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 29.3 tok/s decode · 6.6s TTFT (warm) · 73 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	29.3 tok/s	3606 ms	43K
Coding	A	Runs well	29.3 tok/s	6611 ms	43K
Agentic Coding	A	Tight fit	29.3 tok/s	9617 ms	43K
Reasoning	A	Runs well	29.3 tok/s	7814 ms	43K
RAG	A	Tight fit	27.9 tok/s	12622 ms	43K

Quantization options

How Gemma 3 12B (12B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	A75
Q3_K_S	3	5.9 GB	Low	A76
NVFP4	4

Get started

Copy-paste commands to run Gemma 3 12B on your machine.

Run

ollama run gemma3:12b

Your hardware

More models your Tesla P40 24GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	30.9 tok/s
Qwen 3.5 27B	27B	S	13.4 tok/s

Frequently asked questions

Can Tesla P40 24GB run Gemma 3 12B?

Yes, Tesla P40 24GB can run Gemma 3 12B with a A grade (Runs well). Expected decode speed: 29.3 tok/s.

How much VRAM does Gemma 3 12B need?

Gemma 3 12B (12B parameters) requires approximately 15.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 3 12B?

The recommended quantization for Gemma 3 12B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 3 12B run at on Tesla P40 24GB?

On Tesla P40 24GB, Gemma 3 12B achieves approximately 29.3 tokens per second decode speed with a time-to-first-token of 6611ms using Q4_K_M quantization.

Can Tesla P40 24GB run Gemma 3 12B for coding?

For coding workloads, Gemma 3 12B on Tesla P40 24GB receives a A grade with 29.3 tok/s and 43K context.

What context window can Gemma 3 12B use on Tesla P40 24GB?

On Tesla P40 24GB, Gemma 3 12B can safely use up to 43K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for Tesla P40 24GB See all hardware for Gemma 3 12B

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